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    • 2. 发明授权
    • Occupant type and position detection system
    • 乘员型和位置检测系统
    • US06198998B1
    • 2001-03-06
    • US09368251
    • 1999-08-03
    • Michael E. FarmerMichael P. Bruce
    • Michael E. FarmerMichael P. Bruce
    • G06F1700
    • B60R21/01538B60N2/002B60N2/28G06K9/00201G06K9/00362G06K9/00832G06T7/70
    • A method and system (10) for detecting vehicle occupant type and position utilizes a single camera unit (12) positioned, for example at the driver or passenger side A-pillar, to generate image data of the front seating area of the vehicle. The present invention distinguishes between objects, forwardly or rearwardly facing child seats, and occupants, by periodically mapping the image taken of the interior of the vehicle into an image profile (104), and utilizing image profile matching with stored profile data (110) to determine the occupant or object type. The system and method of the present invention track occupant type and position in both parallel and perpendicular directions relative to a fixed structure such as the vehicle instrument panel to optimize both the efficiency and safety in controlling deployment of a occupant safety device, such as an air bag (28).
    • 用于检测车辆乘员类型和位置的方法和系统(10)利用位于例如驾驶员或乘客侧A柱的单个照相机单元(12)来生成车辆前座椅区域的图像数据。 本发明通过将拍摄到车辆内部的图像周期性地映射到图像轮廓(104)中,并且利用与存储的轮廓数据(110)匹配的图像轮廓来区分通过向前或向后的儿童座椅和乘客的目标, 确定乘客或物体类型。 本发明的系统和方法相对于诸如车辆仪表板的固定结构在平行和垂直方向上跟踪乘员类型和位置,以优化在控制乘员安全装置(例如空气)的部署中的效率和安全性 包(28)。
    • 4. 发明申请
    • Methods and apparatus for classification of occupancy using wavelet transforms
    • 使用小波变换进行占用分类的方法和装置
    • US20080059027A1
    • 2008-03-06
    • US11514299
    • 2006-08-31
    • Michael E. FarmerShweta R. Bapna
    • Michael E. FarmerShweta R. Bapna
    • B60R22/00
    • B60R21/01538
    • Improved methods and apparatus for classifying occupancy of a position use wavelet transforms, such as Gabor filters, for processing images obtained in conjunction therewith. For example, a computer system comprises an algorithm that utilizes a wavelet transform for processing of imagery associated with a position in order to classify occupancy of that position. A method comprises steps of: obtaining an image of the position; optionally segmenting the image at the position; optionally dividing the image into multiple key regions for further analysis; analyzing texture of the image using one or more wavelet transforms; and classifying occupancy of the position based on the texture of the image.
    • 改进了用于对位置使用小波变换占有率进行分类的方法和装置,例如Gabor滤波器,用于处理与其结合获得的图像。 例如,计算机系统包括利用小波变换处理与位置相关联的图像以便对该位置的占用进行分类的算法。 一种方法包括以下步骤:获得所述位置的图像; 可选地在该位置分割图像; 可选地将图像划分成多个关键区域用于进一步分析; 使用一个或多个小波变换分析图像的纹理; 并基于图像的纹理对位置的占用进行分类。
    • 7. 发明授权
    • Neural network radar processor
    • 神经网络雷达处理器
    • US06366236B1
    • 2002-04-02
    • US09637044
    • 2000-08-11
    • Michael E. FarmerCraig S. JacobsShan Cong
    • Michael E. FarmerCraig S. JacobsShan Cong
    • G01S1300
    • G01S13/584B60R21/0134G01S7/417G01S13/34G01S13/931
    • A neural network radar processor (10) comprises a multilayer perceptron neural network (100.1) comprising an input layer (102), a second layer (122), and at least a third layer (124), wherein each layer has a plurality of nodes (108), and respective subsets of nodes (108) of the second (122) and third (124) layers are interconnected so as to form mutually exclusive subnetworks (120). In-phase and quadrature phase time series from a sampled down-converted FMCW radar signal (19) are applied to the input layer, and the neural network (100) is trained so that the nodes of the output layer (106) are responsive to targets in corresponding range cells, and different subnetworks (120) are responsive to respectively different non-overlapping sets of target ranges. The neural network is trained with signals that are germane to an FMCW radar, including a wide range of target scenarios as well as leakage signals, DC bias signals, and background clutter signals.
    • 神经网络雷达处理器(10)包括包括输入层(102),第二层(122)和至少第三层(124)的多层感知器神经网络(100.1),其中每个层具有多个节点 (108),并且第二(122)和第三(124)层的节点(108)的相应子集互连,以便形成相互排斥的子网(120)。 来自采样的下变频FMCW雷达信号(19)的同相和正交相位时间序列被施加到输入层,并且对神经网络(100)进行训练,使得输出层(106)的节点响应于 相应范围单元中的目标,以及不同的子网(120)对目标范围的分别不同的非重叠集合进行响应。 使用与FMCW雷达相关的信号训练神经网络,包括各种目标场景以及泄漏信号,直流偏置信号和背景杂波信号。
    • 9. 发明授权
    • Smart weed recognition/classification system
    • 智能杂草识别/分类系统
    • US5606821A
    • 1997-03-04
    • US257257
    • 1994-07-25
    • Firooz A. SadjadiMichael E. Farmer
    • Firooz A. SadjadiMichael E. Farmer
    • A01M7/00A01G13/00
    • A01M7/0089
    • A smart weed recognition and identification system comprises a chlorophyll sensor for detecting green vegetation and memory map means for storing images which contain different forms of green vegetation. The memory maps stored in memory are processed to eliminate the background information and leave a memory map containing only green vegetation. The enhanced memory map is further processed by an operation of segmentation into identifiable regions and the identifiable green vegetation regions are processed to identify unique attributes for each of the regions. The unique attributes for each of the regions is stored in a reference data base library and are used as reference data for comparing other green vegetation with the data stored in the base model by a processor which matches green vegetation in other regions with the green vegetation stored in said reference data base model and further produces decision data signals which are used by a controller to control a plurality of spray nozzles covering the area sensed and for dispensing a plurality of selectable controlled chemicals.
    • 一种智能杂草识别和识别系统,包括用于检测绿色植被的叶绿素传感器和用于存储包含不同形式的绿色植被的图像的记录图形装置。 处理存储在存储器中的存储器映射以消除背景信息,并留下仅包含绿色植被的存储器映射。 通过将分割操作进一步处理增强的存储器映射,以识别可区域,并且处理可识别的绿色植被区域以识别每个区域的唯一属性。 每个区域的独特属性存储在参考数据库中,并用作参考数据,用于将其他绿色植被与存储在基本模型中的数据进行比较,处理器与其他区域中的绿色植被与绿色植被存储 在所述参考数据库模型中并进一步产生决策数据信号,所述决定数据信号由控制器用来控制覆盖所感测区域并用于分配多个可选择受控化学品的多个喷嘴。
    • 10. 发明授权
    • Programmed radar coordinate scan conversion
    • 编程雷达坐标扫描转换
    • US5519401A
    • 1996-05-21
    • US143597
    • 1993-11-01
    • Michael E. FarmerStephen M. Sohn
    • Michael E. FarmerStephen M. Sohn
    • G01S7/298G06F1/03
    • G06F1/03G01S7/298G06F2101/06
    • A pseudo-code representation and a C language representation of a scan converter system whereby radar amplitude data specified in polar coordinates may be displayed on a computer monitor display controlled by rectangular coordinates is provided. The invention utilizes a look-up table that is built using a two-phase algorithm. The look-up table is set into an initial state after which a mapping process takes place in which all of the (x,y) coordinate values covering the display area are inversely projected to the nearest (r,.theta.) coordinate values using trigonometric and approximation procedures. Since more than one (x,y) value may map to the same (r,.theta.) value, these values are linked together to form a patch. All of the (r,.theta.) coordinates will not be hit in this mapping process. Therefore, a second phase of projection occurs. Each (r,.theta.) coordinate not hit in the aforementioned inverse projection is now projected forward to an (x,y) coordinate using trigonometric and approximation procedures. Upon conclusion of the formation of the look-up table, each (r,.theta.) value will have an associated patch of (x,y) values. The look-up table may now be addressed by r and .theta.. The associated patch contains the x and y coordinate values which are used to paint the display.
    • 提供了扫描转换器系统的伪码表示和C语言表示,其中以极坐标指定的雷达幅度数据可以显示在由直角坐标控制的计算机监视器显示器上。 本发明利用了使用两相算法构建的查找表。 查找表被设置为初始状态,之后进行映射处理,其中使用三角法将覆盖显示区域的所有(x,y)坐标值反向投影到最近的(r,θ)坐标值, 近似程序。 由于多个(x,y)值可能映射到相同的(r,theta)值,所以这些值被链接在一起形成一个补丁。 在这个映射过程中,所有(r,θ)坐标都不会被击中。 因此,发生第二阶段的投影。 在上述反向投影中未命中的每个(r,θ)坐标现在使用三角和近似程序向前投影到(x,y)坐标。 在查找表的形成结束时,每个(r,θ)值将具有(x,y)值的相关补码。 现在可以通过r和θ来查找查找表。 相关补丁包含用于绘制显示的x和y坐标值。